Socioeconomic inequalities in cause specific mortality among older people in France

<p>Abstract</p> <p>Background</p> <p>European comparative studies documented a clear North-South divide in socioeconomic inequalities with cancer being the most important contributor to inequalities in total mortality among middle aged men in Latin Europe (France, Spain, Portugal, Italy). The aim of this paper is to investigate educational inequalities in mortality by gender, age and causes of death in France, with a special emphasis on people aged 75 years and more.</p> <p>Methods</p> <p>We used data from a longitudinal population sample that includes 1% of the French population. Risk of death (total and cause specific) in the period 1990-1999 according to education was analysed using Cox regression models by age group (45-59, 60-74, and 75+). Inequalities were quantified using both relative (ratio) and absolute (difference) measures.</p> <p>Results</p> <p>Relative inequalities decreased with age but were still observed in the oldest age group. Absolute inequalities increased with age. This increase was particularly pronounced for cardiovascular diseases. The contribution of different causes of death to absolute inequalities in total mortality differed between age groups. In particular, the contribution of cancer deaths decreased substantially between the age groups 60-74 years and 75 years and more, both in men and in women.</p> <p>Conclusions</p> <p>This study suggests that the large contribution of cancer deaths to the excess mortality among low educated people that was observed among middle aged men in Latin Europe is not observed among French people aged 75 years and more. This should be confirmed among other Latin Europe countries.</p

The Therapeutic effect of Memantine through the Stimulation of Synapse Formation and Dendritic Spine Maturation in Autism and Fragile X Syndrome

Although the pathogenic mechanisms that underlie autism are not well understood, there is evidence showing that metabotropic and ionotropic glutamate receptors are hyper-stimulated and the GABAergic system is hypo-stimulated in autism. Memantine is an uncompetitive antagonist of NMDA receptors and is widely prescribed for treatment of Alzheimer's disease treatment. Recently, it has been shown to improve language function, social behavior, and self-stimulatory behaviors of some autistic subjects. However the mechanism by which memantine exerts its effect remains to be elucidated. In this study, we used cultured cerebellar granule cells (CGCs) from Fmr1 knockout (KO) mice, a mouse model for fragile X syndrome (FXS) and syndromic autism, to examine the effects of memantine on dendritic spine development and synapse formation. Our results show that the maturation of dendritic spines is delayed in Fmr1-KO CGCs. We also detected reduced excitatory synapse formation in Fmr1-KO CGCs. Memantine treatment of Fmr1-KO CGCs promoted cell adhesion properties. Memantine also stimulated the development of mushroom-shaped mature dendritic spines and restored dendritic spine to normal levels in Fmr1-KO CGCs. Furthermore, we demonstrated that memantine treatment promoted synapse formation and restored the excitatory synapses to a normal range in Fmr1-KO CGCs. These findings suggest that memantine may exert its therapeutic capacity through a stimulatory effect on dendritic spine maturation and excitatory synapse formation, as well as promoting adhesion of CGCs

Assessment of regional best‐fit probability density function of annual maximum rainfall using CFSR precipitation data

The upper Cross River basin (UCRB) fits a true description of a data scarce watershed in respect of climatic data. This paper seeks to determine the best‐fit probability density function (PDF) of annual maximum rainfall for the UCRB using the Climate Forecast System Reanalysis (CFSR) precipitation data. Also, to evaluate the performance of the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Inter‐comparison Project (CMIP3) Fourth Assessment Report (AR4) Global Circulation Models (GCMs) in simulating the monthly precipitation in the UCRB considering 1979–2014 data. For the determination of the best‐fit PDF, the models under review included the generalized extreme value (GEV), normal, gamma, Weibull and log‐normal (LN) distributions. Twenty‐four weather station datasets were obtained and subjected to frequency distribution analysis on per station basis, and subsequently fitted to the respective PDFs. Also, simulated monthly precipitation data obtained from 16 AR4 GCMs, for weather station p6191, were subjected to frequency distribution analysis. The results showed the percentages of best‐fit to worst‐fit PDFs, considering the total number of stations, as follows: 54.17%, 45.83%, 37.50%, 45.83%, and 50%/50%. These percentages corresponded to GEV, Weibull, gamma, gamma, and LN/normal, respectively. The comparison of the predicted and observed values using the Chi‐square goodness‐of‐fit test revealed that the GEV PDF is the best‐fit model for the UCRB. The correlation coefficient values further corroborated the correctness of the test. The PDF of the observed data (weather station p6191) and the simulations of the 16 GCMs computed using monthly rainfall datasets were compared using a mean square error (MSE) dependent skill score. The result from this study suggested that the CGCM3.1 (T47) and MRI‐CGCM2.3.2 provide the best representations of precipitation, considering about 36 years trend for station p6191. The results have no influence on how well the models perform in other geographical locations